3.0 CITE-seq ADT

In [1]:
from clustergrammer2 import net
df = {}

clustergrammer2 backend version 0.2.7
In [2]:
import numpy as np
import pandas as pd
import gene_exp_10x

Load ADT Data

Load ADT data, arcsinh transform the ADT levels, then Z-score ADT levels across cells.

In [3]:
df['adt-ini'] = pd.read_csv('../data/big_data/CITE-seq_CBMC_8K_13AB_10X/GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv', index_col=0)
df['adt'] = np.arcsinh(df['adt-ini']/5)

net.load_df(df['adt'])
net.normalize(axis='row', norm_type='zscore')
df['adt-z'] = net.export_df()
df['adt'].shape
Out[3]:
(13, 8617)

Visualize ADT Levels (Z-scored)

In [4]:
net.load_df(df['adt-z'])
net.widget()
In [ ]: